Description Usage Arguments Details Value Author(s) References See Also Examples

Predict method for `gradientForest`

or `combinedGradientForest`

objects.

1 2 3 4 |

`object` |
an object of class |

`newdata` |
An optional data frame in which to look for variables with which to predict.
If omitted, the environmental variables at the sites in |

`extrap` |
if |

`...` |
further arguments passed to |

The predictor cumulative functions can be used to transform grid data layers of environmental variables to a common biological importance scale. This transformation of the multi-dimensional environment space is to a biological space in which coordinate position represents composition associated with the predictors. These inferred compositional patterns can be mapped in biological space and geographic space in a manner analogous to ordination, that takes into account the non-linear and sometimes threshold changes that occur along gradients.

Where environmental values lie outside the range of the original site data, by default extrapolation
is performed. That is, if `(xmin,xmax)`

are the range of the site predictors with corresponding
cumulative importance values `(ymin,ymax)`

, the prediction `y`

at a new environmental value
outside the range `(xmin,xmax)`

is `ymin + (y-ymin)*(x-xmin)/(xmax-xmin)`

.
This is equivalent to assigning the average importance inside `(xmin,xmax)`

to all values
outside the range. If `extrap=FALSE`

, linear extrapolation is not performed; instead predictions
below `xmin`

are fixed at `ymin`

and predictions above `xmax`

are fixed at `ymax`

.
This is equivalent to assigning zero importance outside the range of the site data.

an object of class `predict.gradientForest`

. It is a dataframe in which each predictor
has been transformed to the biological scale by the cumulative importance
function, as defined by `cumimp`

.

N. Ellis, CSIRO, Cleveland, Australia. <[email protected]>

Ellis, N., Smith, S.J., and Pitcher, C.R. (2012) Gradient Forests: calculating importance
gradients on physical predictors. *Ecology*, **93**, 156–168.

1 2 3 4 5 | ```
data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
f1 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs, ntree=10)
f1.pred<-predict(f1)
``` |

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